Electric Vehicles

This paper addresses the long-standing challenge of attaining high-precision models for LiFePO4 Batteries which suffer from weakly observable dynamics. We introduce a new paradigm of integrating a nonlinear fractional-order physics-based model with a hybrid neural network model. First, a fractional-order model (FOM) is proposed to capture the physics of the battery that existing integer-order models (IOMs) fail to replicate, such as the solid phase diffusion. The FOM parameters are state dependent as they vary along with the progression of the state of charge (SOC). Second, the unknown and...

Decarbonization and electrification of long-haul trucks are notoriously difficult due to the high energy demand and limited gravimetric energy density of lithium-ion cells. In this study, we investigate the optimal deployment and operation of a grid-connected battery swapping station (BSS) for electric long-haul trucks as a mixed-integer optimization problem. We construct a model for reliably meeting customer energy needs while providing grid services, to demonstrate the business case and the operation of such a system. The impact of optimal sizing of the station is explored. A comparative...

Electrification of the truck fleet has the potential to reduce the “harder-to-abate” emissions of logistics significantly, but is generally considered to be very challenging. In this study, we focus on the energy-efficient routing of a mixed fleet of conventional and electric heavy-duty trucks with pickup and delivery under energy consumption uncertainty. We propose an energy consumption model that accounts for realistic driving dynamics, road conditions, weight, and distances. Integrating this model into the routing problem, we address energy consumption uncertainty using second-order...

Improving battery health and safety motivates the synergy of a powerful duo: physics and machine learning. Through seamless integration of these disciplines, the efficacy of mathematical battery models can be significantly enhanced. This paper delves into the challenges and potentials of managing battery health and safety, highlighting the transformative impact of integrating physics and machine learning to address those challenges. Based on our systematic review in this context, we outline several future directions and perspectives, offering a comprehensive exploration of efficient and...